Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Friday 17 July 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 178,928 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 178,918 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 6
## 136 2020-07-14 East of England 1
## 137 2020-07-15 East of England 0
## 138 2020-03-01 London 0
## 139 2020-03-02 London 0
## 140 2020-03-03 London 0
## 141 2020-03-04 London 0
## 142 2020-03-05 London 0
## 143 2020-03-06 London 1
## 144 2020-03-07 London 0
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## 150 2020-03-13 London 10
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## 152 2020-03-15 London 9
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## 154 2020-03-17 London 23
## 155 2020-03-18 London 27
## 156 2020-03-19 London 25
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## 159 2020-03-22 London 54
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## 216 2020-05-18 London 10
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## 220 2020-05-22 London 10
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## 276 2020-03-02 Midlands 0
## 277 2020-03-03 Midlands 1
## 278 2020-03-04 Midlands 0
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## 281 2020-03-07 Midlands 0
## 282 2020-03-08 Midlands 2
## 283 2020-03-09 Midlands 1
## 284 2020-03-10 Midlands 0
## 285 2020-03-11 Midlands 2
## 286 2020-03-12 Midlands 6
## 287 2020-03-13 Midlands 5
## 288 2020-03-14 Midlands 4
## 289 2020-03-15 Midlands 5
## 290 2020-03-16 Midlands 11
## 291 2020-03-17 Midlands 8
## 292 2020-03-18 Midlands 13
## 293 2020-03-19 Midlands 8
## 294 2020-03-20 Midlands 28
## 295 2020-03-21 Midlands 13
## 296 2020-03-22 Midlands 31
## 297 2020-03-23 Midlands 33
## 298 2020-03-24 Midlands 41
## 299 2020-03-25 Midlands 48
## 300 2020-03-26 Midlands 64
## 301 2020-03-27 Midlands 72
## 302 2020-03-28 Midlands 89
## 303 2020-03-29 Midlands 92
## 304 2020-03-30 Midlands 90
## 305 2020-03-31 Midlands 123
## 306 2020-04-01 Midlands 140
## 307 2020-04-02 Midlands 142
## 308 2020-04-03 Midlands 124
## 309 2020-04-04 Midlands 151
## 310 2020-04-05 Midlands 164
## 311 2020-04-06 Midlands 140
## 312 2020-04-07 Midlands 123
## 313 2020-04-08 Midlands 186
## 314 2020-04-09 Midlands 139
## 315 2020-04-10 Midlands 127
## 316 2020-04-11 Midlands 142
## 317 2020-04-12 Midlands 139
## 318 2020-04-13 Midlands 120
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## 332 2020-04-27 Midlands 74
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## 335 2020-04-30 Midlands 56
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## 337 2020-05-02 Midlands 51
## 338 2020-05-03 Midlands 52
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## 341 2020-05-06 Midlands 59
## 342 2020-05-07 Midlands 48
## 343 2020-05-08 Midlands 34
## 344 2020-05-09 Midlands 37
## 345 2020-05-10 Midlands 42
## 346 2020-05-11 Midlands 33
## 347 2020-05-12 Midlands 45
## 348 2020-05-13 Midlands 40
## 349 2020-05-14 Midlands 38
## 350 2020-05-15 Midlands 40
## 351 2020-05-16 Midlands 34
## 352 2020-05-17 Midlands 31
## 353 2020-05-18 Midlands 36
## 354 2020-05-19 Midlands 35
## 355 2020-05-20 Midlands 36
## 356 2020-05-21 Midlands 32
## 357 2020-05-22 Midlands 27
## 358 2020-05-23 Midlands 34
## 359 2020-05-24 Midlands 20
## 360 2020-05-25 Midlands 26
## 361 2020-05-26 Midlands 33
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## 363 2020-05-28 Midlands 28
## 364 2020-05-29 Midlands 20
## 365 2020-05-30 Midlands 21
## 366 2020-05-31 Midlands 22
## 367 2020-06-01 Midlands 20
## 368 2020-06-02 Midlands 22
## 369 2020-06-03 Midlands 24
## 370 2020-06-04 Midlands 16
## 371 2020-06-05 Midlands 21
## 372 2020-06-06 Midlands 20
## 373 2020-06-07 Midlands 17
## 374 2020-06-08 Midlands 16
## 375 2020-06-09 Midlands 18
## 376 2020-06-10 Midlands 15
## 377 2020-06-11 Midlands 13
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## 381 2020-06-15 Midlands 12
## 382 2020-06-16 Midlands 15
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## 406 2020-07-10 Midlands 3
## 407 2020-07-11 Midlands 0
## 408 2020-07-12 Midlands 5
## 409 2020-07-13 Midlands 1
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## 415 2020-03-04 North East and Yorkshire 0
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## 417 2020-03-06 North East and Yorkshire 0
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## 419 2020-03-08 North East and Yorkshire 0
## 420 2020-03-09 North East and Yorkshire 0
## 421 2020-03-10 North East and Yorkshire 0
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## 423 2020-03-12 North East and Yorkshire 0
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## 425 2020-03-14 North East and Yorkshire 0
## 426 2020-03-15 North East and Yorkshire 2
## 427 2020-03-16 North East and Yorkshire 3
## 428 2020-03-17 North East and Yorkshire 1
## 429 2020-03-18 North East and Yorkshire 2
## 430 2020-03-19 North East and Yorkshire 6
## 431 2020-03-20 North East and Yorkshire 5
## 432 2020-03-21 North East and Yorkshire 6
## 433 2020-03-22 North East and Yorkshire 7
## 434 2020-03-23 North East and Yorkshire 9
## 435 2020-03-24 North East and Yorkshire 8
## 436 2020-03-25 North East and Yorkshire 18
## 437 2020-03-26 North East and Yorkshire 21
## 438 2020-03-27 North East and Yorkshire 28
## 439 2020-03-28 North East and Yorkshire 35
## 440 2020-03-29 North East and Yorkshire 38
## 441 2020-03-30 North East and Yorkshire 64
## 442 2020-03-31 North East and Yorkshire 60
## 443 2020-04-01 North East and Yorkshire 67
## 444 2020-04-02 North East and Yorkshire 75
## 445 2020-04-03 North East and Yorkshire 100
## 446 2020-04-04 North East and Yorkshire 105
## 447 2020-04-05 North East and Yorkshire 92
## 448 2020-04-06 North East and Yorkshire 96
## 449 2020-04-07 North East and Yorkshire 102
## 450 2020-04-08 North East and Yorkshire 107
## 451 2020-04-09 North East and Yorkshire 111
## 452 2020-04-10 North East and Yorkshire 117
## 453 2020-04-11 North East and Yorkshire 98
## 454 2020-04-12 North East and Yorkshire 84
## 455 2020-04-13 North East and Yorkshire 94
## 456 2020-04-14 North East and Yorkshire 107
## 457 2020-04-15 North East and Yorkshire 96
## 458 2020-04-16 North East and Yorkshire 103
## 459 2020-04-17 North East and Yorkshire 88
## 460 2020-04-18 North East and Yorkshire 95
## 461 2020-04-19 North East and Yorkshire 88
## 462 2020-04-20 North East and Yorkshire 100
## 463 2020-04-21 North East and Yorkshire 76
## 464 2020-04-22 North East and Yorkshire 84
## 465 2020-04-23 North East and Yorkshire 63
## 466 2020-04-24 North East and Yorkshire 72
## 467 2020-04-25 North East and Yorkshire 69
## 468 2020-04-26 North East and Yorkshire 65
## 469 2020-04-27 North East and Yorkshire 65
## 470 2020-04-28 North East and Yorkshire 57
## 471 2020-04-29 North East and Yorkshire 69
## 472 2020-04-30 North East and Yorkshire 57
## 473 2020-05-01 North East and Yorkshire 64
## 474 2020-05-02 North East and Yorkshire 48
## 475 2020-05-03 North East and Yorkshire 40
## 476 2020-05-04 North East and Yorkshire 49
## 477 2020-05-05 North East and Yorkshire 40
## 478 2020-05-06 North East and Yorkshire 51
## 479 2020-05-07 North East and Yorkshire 45
## 480 2020-05-08 North East and Yorkshire 42
## 481 2020-05-09 North East and Yorkshire 44
## 482 2020-05-10 North East and Yorkshire 40
## 483 2020-05-11 North East and Yorkshire 29
## 484 2020-05-12 North East and Yorkshire 27
## 485 2020-05-13 North East and Yorkshire 28
## 486 2020-05-14 North East and Yorkshire 31
## 487 2020-05-15 North East and Yorkshire 32
## 488 2020-05-16 North East and Yorkshire 35
## 489 2020-05-17 North East and Yorkshire 26
## 490 2020-05-18 North East and Yorkshire 30
## 491 2020-05-19 North East and Yorkshire 27
## 492 2020-05-20 North East and Yorkshire 22
## 493 2020-05-21 North East and Yorkshire 33
## 494 2020-05-22 North East and Yorkshire 22
## 495 2020-05-23 North East and Yorkshire 18
## 496 2020-05-24 North East and Yorkshire 26
## 497 2020-05-25 North East and Yorkshire 21
## 498 2020-05-26 North East and Yorkshire 21
## 499 2020-05-27 North East and Yorkshire 22
## 500 2020-05-28 North East and Yorkshire 21
## 501 2020-05-29 North East and Yorkshire 25
## 502 2020-05-30 North East and Yorkshire 20
## 503 2020-05-31 North East and Yorkshire 20
## 504 2020-06-01 North East and Yorkshire 17
## 505 2020-06-02 North East and Yorkshire 23
## 506 2020-06-03 North East and Yorkshire 23
## 507 2020-06-04 North East and Yorkshire 17
## 508 2020-06-05 North East and Yorkshire 18
## 509 2020-06-06 North East and Yorkshire 21
## 510 2020-06-07 North East and Yorkshire 14
## 511 2020-06-08 North East and Yorkshire 11
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## 513 2020-06-10 North East and Yorkshire 19
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## 527 2020-06-24 North East and Yorkshire 10
## 528 2020-06-25 North East and Yorkshire 4
## 529 2020-06-26 North East and Yorkshire 7
## 530 2020-06-27 North East and Yorkshire 3
## 531 2020-06-28 North East and Yorkshire 5
## 532 2020-06-29 North East and Yorkshire 2
## 533 2020-06-30 North East and Yorkshire 5
## 534 2020-07-01 North East and Yorkshire 1
## 535 2020-07-02 North East and Yorkshire 4
## 536 2020-07-03 North East and Yorkshire 3
## 537 2020-07-04 North East and Yorkshire 4
## 538 2020-07-05 North East and Yorkshire 2
## 539 2020-07-06 North East and Yorkshire 2
## 540 2020-07-07 North East and Yorkshire 3
## 541 2020-07-08 North East and Yorkshire 3
## 542 2020-07-09 North East and Yorkshire 0
## 543 2020-07-10 North East and Yorkshire 3
## 544 2020-07-11 North East and Yorkshire 1
## 545 2020-07-12 North East and Yorkshire 4
## 546 2020-07-13 North East and Yorkshire 1
## 547 2020-07-14 North East and Yorkshire 1
## 548 2020-07-15 North East and Yorkshire 1
## 549 2020-03-01 North West 0
## 550 2020-03-02 North West 0
## 551 2020-03-03 North West 0
## 552 2020-03-04 North West 0
## 553 2020-03-05 North West 1
## 554 2020-03-06 North West 0
## 555 2020-03-07 North West 0
## 556 2020-03-08 North West 1
## 557 2020-03-09 North West 0
## 558 2020-03-10 North West 0
## 559 2020-03-11 North West 0
## 560 2020-03-12 North West 2
## 561 2020-03-13 North West 3
## 562 2020-03-14 North West 1
## 563 2020-03-15 North West 4
## 564 2020-03-16 North West 2
## 565 2020-03-17 North West 4
## 566 2020-03-18 North West 6
## 567 2020-03-19 North West 7
## 568 2020-03-20 North West 10
## 569 2020-03-21 North West 11
## 570 2020-03-22 North West 13
## 571 2020-03-23 North West 15
## 572 2020-03-24 North West 21
## 573 2020-03-25 North West 21
## 574 2020-03-26 North West 29
## 575 2020-03-27 North West 36
## 576 2020-03-28 North West 28
## 577 2020-03-29 North West 46
## 578 2020-03-30 North West 67
## 579 2020-03-31 North West 52
## 580 2020-04-01 North West 86
## 581 2020-04-02 North West 96
## 582 2020-04-03 North West 95
## 583 2020-04-04 North West 98
## 584 2020-04-05 North West 102
## 585 2020-04-06 North West 100
## 586 2020-04-07 North West 135
## 587 2020-04-08 North West 127
## 588 2020-04-09 North West 119
## 589 2020-04-10 North West 117
## 590 2020-04-11 North West 138
## 591 2020-04-12 North West 125
## 592 2020-04-13 North West 129
## 593 2020-04-14 North West 131
## 594 2020-04-15 North West 114
## 595 2020-04-16 North West 135
## 596 2020-04-17 North West 98
## 597 2020-04-18 North West 113
## 598 2020-04-19 North West 71
## 599 2020-04-20 North West 83
## 600 2020-04-21 North West 76
## 601 2020-04-22 North West 86
## 602 2020-04-23 North West 85
## 603 2020-04-24 North West 66
## 604 2020-04-25 North West 66
## 605 2020-04-26 North West 55
## 606 2020-04-27 North West 54
## 607 2020-04-28 North West 57
## 608 2020-04-29 North West 63
## 609 2020-04-30 North West 59
## 610 2020-05-01 North West 45
## 611 2020-05-02 North West 56
## 612 2020-05-03 North West 55
## 613 2020-05-04 North West 48
## 614 2020-05-05 North West 48
## 615 2020-05-06 North West 44
## 616 2020-05-07 North West 49
## 617 2020-05-08 North West 42
## 618 2020-05-09 North West 31
## 619 2020-05-10 North West 42
## 620 2020-05-11 North West 35
## 621 2020-05-12 North West 38
## 622 2020-05-13 North West 25
## 623 2020-05-14 North West 26
## 624 2020-05-15 North West 33
## 625 2020-05-16 North West 32
## 626 2020-05-17 North West 24
## 627 2020-05-18 North West 31
## 628 2020-05-19 North West 35
## 629 2020-05-20 North West 27
## 630 2020-05-21 North West 27
## 631 2020-05-22 North West 26
## 632 2020-05-23 North West 31
## 633 2020-05-24 North West 26
## 634 2020-05-25 North West 31
## 635 2020-05-26 North West 27
## 636 2020-05-27 North West 27
## 637 2020-05-28 North West 28
## 638 2020-05-29 North West 20
## 639 2020-05-30 North West 19
## 640 2020-05-31 North West 13
## 641 2020-06-01 North West 12
## 642 2020-06-02 North West 27
## 643 2020-06-03 North West 22
## 644 2020-06-04 North West 22
## 645 2020-06-05 North West 16
## 646 2020-06-06 North West 26
## 647 2020-06-07 North West 20
## 648 2020-06-08 North West 23
## 649 2020-06-09 North West 17
## 650 2020-06-10 North West 16
## 651 2020-06-11 North West 16
## 652 2020-06-12 North West 11
## 653 2020-06-13 North West 10
## 654 2020-06-14 North West 15
## 655 2020-06-15 North West 16
## 656 2020-06-16 North West 15
## 657 2020-06-17 North West 13
## 658 2020-06-18 North West 13
## 659 2020-06-19 North West 7
## 660 2020-06-20 North West 11
## 661 2020-06-21 North West 7
## 662 2020-06-22 North West 11
## 663 2020-06-23 North West 13
## 664 2020-06-24 North West 13
## 665 2020-06-25 North West 15
## 666 2020-06-26 North West 6
## 667 2020-06-27 North West 7
## 668 2020-06-28 North West 9
## 669 2020-06-29 North West 8
## 670 2020-06-30 North West 6
## 671 2020-07-01 North West 3
## 672 2020-07-02 North West 6
## 673 2020-07-03 North West 6
## 674 2020-07-04 North West 4
## 675 2020-07-05 North West 6
## 676 2020-07-06 North West 9
## 677 2020-07-07 North West 7
## 678 2020-07-08 North West 5
## 679 2020-07-09 North West 10
## 680 2020-07-10 North West 2
## 681 2020-07-11 North West 3
## 682 2020-07-12 North West 0
## 683 2020-07-13 North West 6
## 684 2020-07-14 North West 2
## 685 2020-07-15 North West 1
## 686 2020-03-01 South East 0
## 687 2020-03-02 South East 0
## 688 2020-03-03 South East 1
## 689 2020-03-04 South East 0
## 690 2020-03-05 South East 1
## 691 2020-03-06 South East 0
## 692 2020-03-07 South East 0
## 693 2020-03-08 South East 1
## 694 2020-03-09 South East 1
## 695 2020-03-10 South East 1
## 696 2020-03-11 South East 1
## 697 2020-03-12 South East 0
## 698 2020-03-13 South East 1
## 699 2020-03-14 South East 1
## 700 2020-03-15 South East 5
## 701 2020-03-16 South East 8
## 702 2020-03-17 South East 7
## 703 2020-03-18 South East 10
## 704 2020-03-19 South East 9
## 705 2020-03-20 South East 13
## 706 2020-03-21 South East 7
## 707 2020-03-22 South East 25
## 708 2020-03-23 South East 20
## 709 2020-03-24 South East 22
## 710 2020-03-25 South East 29
## 711 2020-03-26 South East 35
## 712 2020-03-27 South East 34
## 713 2020-03-28 South East 36
## 714 2020-03-29 South East 55
## 715 2020-03-30 South East 58
## 716 2020-03-31 South East 65
## 717 2020-04-01 South East 66
## 718 2020-04-02 South East 55
## 719 2020-04-03 South East 72
## 720 2020-04-04 South East 80
## 721 2020-04-05 South East 82
## 722 2020-04-06 South East 88
## 723 2020-04-07 South East 100
## 724 2020-04-08 South East 83
## 725 2020-04-09 South East 104
## 726 2020-04-10 South East 88
## 727 2020-04-11 South East 88
## 728 2020-04-12 South East 88
## 729 2020-04-13 South East 84
## 730 2020-04-14 South East 65
## 731 2020-04-15 South East 72
## 732 2020-04-16 South East 56
## 733 2020-04-17 South East 86
## 734 2020-04-18 South East 57
## 735 2020-04-19 South East 70
## 736 2020-04-20 South East 87
## 737 2020-04-21 South East 51
## 738 2020-04-22 South East 54
## 739 2020-04-23 South East 57
## 740 2020-04-24 South East 64
## 741 2020-04-25 South East 51
## 742 2020-04-26 South East 51
## 743 2020-04-27 South East 41
## 744 2020-04-28 South East 40
## 745 2020-04-29 South East 47
## 746 2020-04-30 South East 29
## 747 2020-05-01 South East 37
## 748 2020-05-02 South East 36
## 749 2020-05-03 South East 17
## 750 2020-05-04 South East 35
## 751 2020-05-05 South East 29
## 752 2020-05-06 South East 25
## 753 2020-05-07 South East 27
## 754 2020-05-08 South East 26
## 755 2020-05-09 South East 28
## 756 2020-05-10 South East 19
## 757 2020-05-11 South East 25
## 758 2020-05-12 South East 27
## 759 2020-05-13 South East 18
## 760 2020-05-14 South East 32
## 761 2020-05-15 South East 25
## 762 2020-05-16 South East 22
## 763 2020-05-17 South East 18
## 764 2020-05-18 South East 22
## 765 2020-05-19 South East 12
## 766 2020-05-20 South East 22
## 767 2020-05-21 South East 15
## 768 2020-05-22 South East 17
## 769 2020-05-23 South East 21
## 770 2020-05-24 South East 17
## 771 2020-05-25 South East 13
## 772 2020-05-26 South East 19
## 773 2020-05-27 South East 18
## 774 2020-05-28 South East 12
## 775 2020-05-29 South East 21
## 776 2020-05-30 South East 8
## 777 2020-05-31 South East 12
## 778 2020-06-01 South East 11
## 779 2020-06-02 South East 13
## 780 2020-06-03 South East 18
## 781 2020-06-04 South East 11
## 782 2020-06-05 South East 11
## 783 2020-06-06 South East 10
## 784 2020-06-07 South East 12
## 785 2020-06-08 South East 8
## 786 2020-06-09 South East 10
## 787 2020-06-10 South East 11
## 788 2020-06-11 South East 5
## 789 2020-06-12 South East 6
## 790 2020-06-13 South East 7
## 791 2020-06-14 South East 7
## 792 2020-06-15 South East 8
## 793 2020-06-16 South East 13
## 794 2020-06-17 South East 9
## 795 2020-06-18 South East 4
## 796 2020-06-19 South East 7
## 797 2020-06-20 South East 5
## 798 2020-06-21 South East 3
## 799 2020-06-22 South East 2
## 800 2020-06-23 South East 8
## 801 2020-06-24 South East 7
## 802 2020-06-25 South East 5
## 803 2020-06-26 South East 8
## 804 2020-06-27 South East 8
## 805 2020-06-28 South East 6
## 806 2020-06-29 South East 5
## 807 2020-06-30 South East 5
## 808 2020-07-01 South East 2
## 809 2020-07-02 South East 7
## 810 2020-07-03 South East 3
## 811 2020-07-04 South East 5
## 812 2020-07-05 South East 4
## 813 2020-07-06 South East 3
## 814 2020-07-07 South East 5
## 815 2020-07-08 South East 3
## 816 2020-07-09 South East 7
## 817 2020-07-10 South East 3
## 818 2020-07-11 South East 1
## 819 2020-07-12 South East 2
## 820 2020-07-13 South East 4
## 821 2020-07-14 South East 1
## 822 2020-07-15 South East 0
## 823 2020-03-01 South West 0
## 824 2020-03-02 South West 0
## 825 2020-03-03 South West 0
## 826 2020-03-04 South West 0
## 827 2020-03-05 South West 0
## 828 2020-03-06 South West 0
## 829 2020-03-07 South West 0
## 830 2020-03-08 South West 0
## 831 2020-03-09 South West 0
## 832 2020-03-10 South West 0
## 833 2020-03-11 South West 1
## 834 2020-03-12 South West 0
## 835 2020-03-13 South West 0
## 836 2020-03-14 South West 1
## 837 2020-03-15 South West 0
## 838 2020-03-16 South West 0
## 839 2020-03-17 South West 2
## 840 2020-03-18 South West 2
## 841 2020-03-19 South West 4
## 842 2020-03-20 South West 3
## 843 2020-03-21 South West 6
## 844 2020-03-22 South West 7
## 845 2020-03-23 South West 8
## 846 2020-03-24 South West 7
## 847 2020-03-25 South West 9
## 848 2020-03-26 South West 11
## 849 2020-03-27 South West 13
## 850 2020-03-28 South West 21
## 851 2020-03-29 South West 18
## 852 2020-03-30 South West 23
## 853 2020-03-31 South West 23
## 854 2020-04-01 South West 21
## 855 2020-04-02 South West 23
## 856 2020-04-03 South West 30
## 857 2020-04-04 South West 42
## 858 2020-04-05 South West 32
## 859 2020-04-06 South West 34
## 860 2020-04-07 South West 39
## 861 2020-04-08 South West 47
## 862 2020-04-09 South West 24
## 863 2020-04-10 South West 46
## 864 2020-04-11 South West 43
## 865 2020-04-12 South West 23
## 866 2020-04-13 South West 27
## 867 2020-04-14 South West 24
## 868 2020-04-15 South West 32
## 869 2020-04-16 South West 29
## 870 2020-04-17 South West 33
## 871 2020-04-18 South West 25
## 872 2020-04-19 South West 31
## 873 2020-04-20 South West 26
## 874 2020-04-21 South West 26
## 875 2020-04-22 South West 23
## 876 2020-04-23 South West 17
## 877 2020-04-24 South West 19
## 878 2020-04-25 South West 15
## 879 2020-04-26 South West 27
## 880 2020-04-27 South West 13
## 881 2020-04-28 South West 17
## 882 2020-04-29 South West 15
## 883 2020-04-30 South West 26
## 884 2020-05-01 South West 6
## 885 2020-05-02 South West 7
## 886 2020-05-03 South West 10
## 887 2020-05-04 South West 17
## 888 2020-05-05 South West 14
## 889 2020-05-06 South West 19
## 890 2020-05-07 South West 16
## 891 2020-05-08 South West 6
## 892 2020-05-09 South West 11
## 893 2020-05-10 South West 5
## 894 2020-05-11 South West 8
## 895 2020-05-12 South West 7
## 896 2020-05-13 South West 7
## 897 2020-05-14 South West 6
## 898 2020-05-15 South West 4
## 899 2020-05-16 South West 4
## 900 2020-05-17 South West 6
## 901 2020-05-18 South West 4
## 902 2020-05-19 South West 6
## 903 2020-05-20 South West 1
## 904 2020-05-21 South West 9
## 905 2020-05-22 South West 6
## 906 2020-05-23 South West 6
## 907 2020-05-24 South West 3
## 908 2020-05-25 South West 8
## 909 2020-05-26 South West 11
## 910 2020-05-27 South West 5
## 911 2020-05-28 South West 10
## 912 2020-05-29 South West 7
## 913 2020-05-30 South West 3
## 914 2020-05-31 South West 2
## 915 2020-06-01 South West 7
## 916 2020-06-02 South West 2
## 917 2020-06-03 South West 7
## 918 2020-06-04 South West 2
## 919 2020-06-05 South West 2
## 920 2020-06-06 South West 1
## 921 2020-06-07 South West 3
## 922 2020-06-08 South West 3
## 923 2020-06-09 South West 0
## 924 2020-06-10 South West 1
## 925 2020-06-11 South West 2
## 926 2020-06-12 South West 2
## 927 2020-06-13 South West 2
## 928 2020-06-14 South West 0
## 929 2020-06-15 South West 2
## 930 2020-06-16 South West 2
## 931 2020-06-17 South West 0
## 932 2020-06-18 South West 0
## 933 2020-06-19 South West 0
## 934 2020-06-20 South West 2
## 935 2020-06-21 South West 0
## 936 2020-06-22 South West 1
## 937 2020-06-23 South West 1
## 938 2020-06-24 South West 1
## 939 2020-06-25 South West 0
## 940 2020-06-26 South West 3
## 941 2020-06-27 South West 0
## 942 2020-06-28 South West 0
## 943 2020-06-29 South West 1
## 944 2020-06-30 South West 0
## 945 2020-07-01 South West 0
## 946 2020-07-02 South West 0
## 947 2020-07-03 South West 0
## 948 2020-07-04 South West 0
## 949 2020-07-05 South West 1
## 950 2020-07-06 South West 0
## 951 2020-07-07 South West 0
## 952 2020-07-08 South West 2
## 953 2020-07-09 South West 0
## 954 2020-07-10 South West 1
## 955 2020-07-11 South West 0
## 956 2020-07-12 South West 0
## 957 2020-07-13 South West 1
## 958 2020-07-14 South West 0
## 959 2020-07-15 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 16 Jul 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -13.1961 -4.1881 -0.2234 3.8028 8.2223
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.604e+00 6.417e-02 71.75 <2e-16 ***
## note_lag 1.447e-05 6.675e-07 21.67 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 23.61847)
##
## Null deviance: 12015.1 on 75 degrees of freedom
## Residual deviance: 1846.3 on 74 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 99.898121 1.000014
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 87.919137 113.070812
## note_lag 1.000013 1.000016
Rsq(lag_mod)
## [1] 0.8463367
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.4.2 webshot_0.5.2 httr_1.4.1
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-8 zip_2.0.4 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.6 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.12.8 modelr_0.1.8 vctrs_0.3.2 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.15
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1